{
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  "metadata": {
    "colab": {
      "name": "W5_Tutorial2_instructor_notebook.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gvoa11a7eS91"
      },
      "source": [
        "# CIS 522 Week 5: Regularization\n",
        "\n",
        "\n",
        "__Instructor:__ Lyle Ungar\n",
        "\n",
        "__Content creators:__ Ravi Teja Konkimalla, Mohitrajhu Lingan Kumaraian"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oI04YQ_2IVm8"
      },
      "source": [
        "#### Ensure you're running a GPU notebook.\n",
        "\n",
        "From \"Runtime\" in the drop-down menu above, click \"Change runtime type\". Ensure that \"Hardware Accelerator\" says \"GPU\".\n",
        "\n",
        "#### Ensure you can save!\n",
        "\n",
        "From \"File\", click \"Save a copy in Drive\""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QcARZs7tbHIi",
        "cellView": "form"
      },
      "source": [
        "#@title Import Libraries\n",
        "from __future__ import print_function\n",
        "import torch\n",
        "import pathlib\n",
        "import random\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torchvision import datasets, transforms\n",
        "from torchvision.datasets import ImageFolder\n",
        "from torch.utils.data import DataLoader, TensorDataset\n",
        "import torch.nn.utils.prune as prune\n",
        "from torch.optim.lr_scheduler import StepLR\n",
        "import time\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.animation as animation\n",
        "import copy\n",
        "from tqdm import tqdm"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OLIyaJBeIDs1",
        "cellView": "form"
      },
      "source": [
        "#@markdown What is your Pennkey and pod? (text, not numbers, e.g. bfranklin)\n",
        "my_pennkey = 'ravi' #@param {type:\"string\"}\n",
        "my_pod = 'nonchalant-crocodile' #@param ['Select', 'euclidean-wombat', 'sublime-newt', 'buoyant-unicorn', 'lackadaisical-manatee','indelible-stingray','superfluous-lyrebird','discreet-reindeer','quizzical-goldfish','astute-jellyfish','ubiquitous-cheetah','nonchalant-crocodile','fashionable-lemur','spiffy-eagle','electric-emu','quotidian-lion']\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LU2phW9PrXnP"
      },
      "source": [
        "#Question of the Week\n",
        "Why does it work better to regularize an overparameterized ANN than to start with a smaller one? [think about  the regularization  methods you know]\n",
        "Each group has a 10 min discussion."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6QSIMHQhr0T9",
        "cellView": "form"
      },
      "source": [
        "#@markdown Summerize your discussion\n",
        "question_of_the_week = 'brodha V' #@param {type:\"string\"}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GvXxa4b2l4m4"
      },
      "source": [
        "# Setup\n",
        "Note that some of the code for today can take up to an hour to run. We have therefore \"hidden\" that code and shown the resulting outputs.\n",
        "\n",
        "[Here](https://docs.google.com/presentation/d/1n4eA5VGG8ab0mkW1kJK5egaldJR4cnpFAHDVbkVPnRI/edit#slide=id.gb88533964a_0_198) are the slides for today's videos (in case you want to take notes). **Do not read them now.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wDBtaMET-fNA",
        "cellView": "form"
      },
      "source": [
        "# @title Figure Settings\n",
        "import ipywidgets as widgets\n",
        "%matplotlib inline \n",
        "fig_w, fig_h = (8, 6)\n",
        "plt.rcParams.update({'figure.figsize': (fig_w, fig_h)})\n",
        "%config InlineBackend.figure_format = 'retina'\n",
        "SMALL_SIZE = 12\n",
        "\n",
        "plt.rcParams.update(plt.rcParamsDefault)\n",
        "plt.rc('animation', html='jshtml')\n",
        "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
        "plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title\n",
        "plt.rc('axes', labelsize=SMALL_SIZE)    # fontsize of the x and y labels\n",
        "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
        "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
        "plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
        "plt.rc('figure', titlesize=SMALL_SIZE)  # fontsize of the figure title"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mvhx88j7e6m2",
        "cellView": "form"
      },
      "source": [
        "# @title Loading Animal Faces data\n",
        "%%capture\n",
        "!rm -r AnimalFaces32x32/\n",
        "!git clone https://github.com/arashash/AnimalFaces32x32\n",
        "!rm -r afhq/\n",
        "!unzip ./AnimalFaces32x32/afhq_32x32.zip"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RMuvvw3VHm1Z",
        "cellView": "form"
      },
      "source": [
        "#@title Seeding for Reproducibility\n",
        "seed = 90108\n",
        "random.seed(seed)\n",
        "np.random.seed(seed)\n",
        "torch.manual_seed(seed)\n",
        "torch.cuda.manual_seed(seed)\n",
        "torch.cuda.manual_seed_all(seed)\n",
        "torch.backends.cudnn.deterministic = True\n",
        "torch.backends.cudnn.benchmark = True\n",
        "torch.backends.cudnn.enabled = True\n",
        "torch.set_deterministic(True)\n",
        "def seed_worker(worker_id):\n",
        "    worker_seed = seed % (worker_id+1)\n",
        "    np.random.seed(worker_seed)\n",
        "    random.seed(worker_seed)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RxCVV-2yt-GX",
        "cellView": "form"
      },
      "source": [
        "# @title Helper functions\r\n",
        "def imshow(img):\r\n",
        "    img = img / 2 + 0.5\r\n",
        "    npimg = img.numpy()\r\n",
        "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\r\n",
        "    plt.axis(False)\r\n",
        "    plt.show()\r\n",
        "\r\n",
        "def train(args, model, device, train_loader, optimizer, epoch,reg_function1=None,reg_function2=None,criterion=F.nll_loss):\r\n",
        "    model.train()\r\n",
        "    for batch_idx, (data, target) in enumerate(train_loader):\r\n",
        "        data, target = data.to(device), target.to(device)\r\n",
        "        optimizer.zero_grad()\r\n",
        "        output = model(data)\r\n",
        "        if reg_function1 is None:\r\n",
        "          loss = criterion(output, target)\r\n",
        "        elif reg_function2 is None:\r\n",
        "          loss = criterion(output, target)+args['lambda']*reg_function1(model)\r\n",
        "        else:\r\n",
        "          loss = criterion(output, target)+args['lambda1']*reg_function1(model)+args['lambda2']*reg_function2(model)\r\n",
        "        loss.backward()\r\n",
        "        optimizer.step()\r\n",
        "        # if (batch_idx % args['log_interval'] == 0 and batch_idx != 0):\r\n",
        "            # print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\r\n",
        "                # epoch, batch_idx * len(data), len(train_loader.dataset),\r\n",
        "                # 100. * batch_idx / len(train_loader), loss.item()))\r\n",
        "\r\n",
        "def test(model, device, test_loader, loader = 'Test',criterion=F.nll_loss):\r\n",
        "    model.eval()\r\n",
        "    test_loss = 0\r\n",
        "    correct = 0\r\n",
        "    with torch.no_grad():\r\n",
        "        for data, target in test_loader:\r\n",
        "            data, target = data.to(device), target.to(device)\r\n",
        "            output = model(data)\r\n",
        "            test_loss += criterion(output, target, reduction='sum').item()  # sum up batch loss\r\n",
        "            pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability\r\n",
        "            correct += pred.eq(target.view_as(pred)).sum().item()\r\n",
        "\r\n",
        "    test_loss /= len(test_loader.dataset)\r\n",
        "\r\n",
        "    # print('\\n{} set: Average loss: {:.4f}, Accuracy: {}/{} ({:.4f}%)\\n'.format(\r\n",
        "        # loader, test_loss, correct, len(test_loader.dataset),\r\n",
        "        # 100. * correct / len(test_loader.dataset)))\r\n",
        "    return 100. * correct / len(test_loader.dataset)\r\n",
        "\r\n",
        "def main(args, model,train_loader,val_loader,test_data,reg_function1=None,reg_function2=None,criterion=F.nll_loss):\r\n",
        "  \"\"\"\r\n",
        "  Trains the model with train_loader and tests the learned model using val_loader\r\n",
        "  \"\"\"\r\n",
        "\r\n",
        "  use_cuda = not args['no_cuda'] and torch.cuda.is_available()\r\n",
        "  device = torch.device('cuda' if use_cuda else 'cpu') \r\n",
        "\r\n",
        "  model = model.to(device)\r\n",
        "  optimizer = optim.SGD(model.parameters(), lr=args['lr'], momentum=args['momentum'])\r\n",
        "\r\n",
        "  best_acc  = 0.0\r\n",
        "  best_epoch = 0\r\n",
        "\r\n",
        "  val_acc_list, train_acc_list,param_norm_list = [], [], []\r\n",
        "  for epoch in tqdm(range(args['epochs'])):\r\n",
        "      train(args, model, device, train_loader, optimizer, epoch,reg_function1=reg_function1,reg_function2=reg_function2)\r\n",
        "      train_acc = test(model,device,train_loader, 'Train')\r\n",
        "      val_acc = test(model,device,val_loader, 'Val')\r\n",
        "      param_norm = calculate_frobenius_norm(model)\r\n",
        "      train_acc_list.append(train_acc)\r\n",
        "      val_acc_list.append(val_acc)\r\n",
        "      param_norm_list.append(param_norm)\r\n",
        "\r\n",
        "  return val_acc_list, train_acc_list, param_norm_list, model, best_epoch\r\n",
        "\r\n",
        "def calculate_frobenius_norm(model):\r\n",
        "    norm = 0.0\r\n",
        "\r\n",
        "    for name,param in model.named_parameters():\r\n",
        "        norm += torch.norm(param).data**2\r\n",
        "    return norm**0.5\r\n",
        "\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QSfEJun00dwZ",
        "cellView": "form"
      },
      "source": [
        "# @title Network Classes for Animal Faces\n",
        "class Animal_Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Animal_Net, self).__init__()\n",
        "        self.fc1 = nn.Linear(3*32*32, 128)\n",
        "        self.fc2 = nn.Linear(128, 32)\n",
        "        self.fc3 = nn.Linear(32, 3)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = x.view(x.shape[0],-1)\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = F.relu(self.fc2(x))\n",
        "        x = self.fc3(x)\n",
        "        output = F.log_softmax(x, dim=1)\n",
        "        return output\n",
        "\n",
        "\n",
        "class Big_Animal_Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        torch.manual_seed(104)\n",
        "        super(Big_Animal_Net, self).__init__()\n",
        "        self.fc1 = nn.Linear(3*32*32, 124)\n",
        "        self.fc2 = nn.Linear(124, 64)\n",
        "        self.fc3 = nn.Linear(64, 3)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = x.view(x.shape[0],-1)\n",
        "        x = F.leaky_relu(self.fc1(x))\n",
        "        x = F.leaky_relu(self.fc2(x))\n",
        "        x = self.fc3(x)\n",
        "        output = F.log_softmax(x, dim=1)\n",
        "        return output"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QGBBuMD3vSvT",
        "cellView": "form"
      },
      "source": [
        "# @title Dataloder\r\n",
        "batch_size = 128\r\n",
        "classes = ('cat', 'dog', 'wild')\r\n",
        "\r\n",
        "train_transform = transforms.Compose([\r\n",
        "     transforms.RandomRotation(10),\r\n",
        "     transforms.RandomHorizontalFlip(),\r\n",
        "     transforms.ToTensor(),\r\n",
        "     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))  \r\n",
        "     ])\r\n",
        "\r\n",
        "data_path = pathlib.Path('.')/'afhq' # using pathlib to be compatible with all OS's\r\n",
        "img_dataset = ImageFolder(data_path/'train', transform=train_transform)\r\n",
        "img_train_data, img_val_data,_ = torch.utils.data.random_split(img_dataset, [100,100,14430])\r\n",
        "\r\n",
        "train_loader = torch.utils.data.DataLoader(img_train_data,batch_size=batch_size,worker_init_fn=seed_worker)\r\n",
        "val_loader = torch.utils.data.DataLoader(img_val_data,batch_size=1000,worker_init_fn=seed_worker)\r\n",
        "\r\n",
        "test_transform = transforms.Compose([\r\n",
        "     transforms.ToTensor(),\r\n",
        "     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\r\n",
        "     ])\r\n",
        "img_test_dataset = ImageFolder(data_path/'val', transform=test_transform)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3qaOATxHS8TR"
      },
      "source": [
        "#Section 1: Stochastic Gradient Descent\n",
        "\n",
        "(Time Estimate: 15 min from start)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DV1QFxoRpHm2",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "outputId": "07a5ee0c-8674-4925-e995-abaa9256aec4"
      },
      "source": [
        "#@title Video : SGD\n",
        "try: t1;\n",
        "except NameError: t1=time.time()\n",
        "\n",
        "from IPython.display import YouTubeVideo\n",
        "video = YouTubeVideo(id=\"E3g2Z-ZqMZw\", width=854, height=480, fs=1)\n",
        "print(\"Video available at https://youtube.com/watch?v=\" + video.id)\n",
        "\n",
        "video"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Video available at https://youtube.com/watch?v=E3g2Z-ZqMZw\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "            width=\"854\"\n",
              "            height=\"480\"\n",
              "            src=\"https://www.youtube.com/embed/E3g2Z-ZqMZw?fs=1\"\n",
              "            frameborder=\"0\"\n",
              "            allowfullscreen\n",
              "        ></iframe>\n",
              "        "
            ],
            "text/plain": [
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            "image/jpeg": 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\n"
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hxRoCUOaXAXz"
      },
      "source": [
        "## Learning Rate\n",
        "In this section below we will see how learning rate can act as regularizer while training the neural network. In summary:\n",
        "\n",
        "\n",
        "*   Smaller Learning rate does not regularize well. Rather it slowly converges to a local deep minima. \n",
        "*   Larger Learning rate regularizes well by missing local minimas and finding a broader, flatter minima. These minima may be more robust.\n",
        "\n",
        "But beware, taking a very large learning rate may result in overshooting or finding a really bad local minima.\n",
        "\n",
        "\n",
        "\n",
        "In the below block we will train the Animal Net model with different learning rates and see how that is going to affect the regularization performance."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-sbY2JYF1maP"
      },
      "source": [
        "#@title Generating Data Loaders\r\n",
        "batch_size = 128\r\n",
        "train_transform = transforms.Compose([\r\n",
        "     transforms.ToTensor(),\r\n",
        "     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))    \r\n",
        "     ])\r\n",
        "\r\n",
        "data_path = pathlib.Path('.')/'afhq' # using pathlib to be compatible with all OS's\r\n",
        "img_dataset = ImageFolder(data_path/'train', transform=train_transform)\r\n",
        "img_train_data, img_val_data, = torch.utils.data.random_split(img_dataset, [11700,2930])\r\n",
        "\r\n",
        "full_train_loader = torch.utils.data.DataLoader(img_train_data,batch_size=batch_size,num_workers=4,worker_init_fn=seed_worker)\r\n",
        "full_val_loader = torch.utils.data.DataLoader(img_val_data,batch_size=1000,num_workers=4,worker_init_fn=seed_worker)\r\n",
        "\r\n",
        "test_transform = transforms.Compose([\r\n",
        "     transforms.ToTensor(),\r\n",
        "     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))    # [TO-DO]\r\n",
        "     ])\r\n",
        "img_test_dataset = ImageFolder(data_path/'val', transform=test_transform)\r\n",
        "# img_test_loader = DataLoader(img_test_dataset, batch_size=batch_size,shuffle=False, num_workers=1)\r\n",
        "classes = ('cat', 'dog', 'wild')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FFWmkt9O-047",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a6df2c7a-6c85-429e-fe32-941177516cc2"
      },
      "source": [
        "args = {'test_batch_size': 1000,\n",
        "        'epochs': 350,\n",
        "        'batch_size': 32,\n",
        "        'momentum': 0.99,\n",
        "        'no_cuda': False\n",
        "        }\n",
        "\n",
        "lr = [5e-4,1e-3, 5e-3]\n",
        "acc_dict = {}\n",
        "\n",
        "for i in range(len(lr)):\n",
        "    model = Animal_Net()\n",
        "    args['lr'] = lr[i]\n",
        "    val_acc, train_acc, param_norm,_,_ = main(args,model,train_loader,val_loader,img_test_dataset)\n",
        "    acc_dict['val_'+str(i)] = val_acc\n",
        "    acc_dict['train_'+str(i)] = train_acc\n",
        "    acc_dict['param_norm'+str(i)] = param_norm"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 350/350 [00:56<00:00,  6.16it/s]\n",
            "100%|██████████| 350/350 [00:57<00:00,  6.06it/s]\n",
            "100%|██████████| 350/350 [00:56<00:00,  6.19it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "80-gI9BJU1Dm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        },
        "outputId": "c9417e9c-be62-4218-efaf-69a80207c3a9"
      },
      "source": [
        "plt.plot(acc_dict['val_0'],linestyle='dashed', label='lr = 5e-4 - validation', c = 'blue')\n",
        "plt.plot(acc_dict['train_0'],label = '5e-4 - train', c = 'blue')\n",
        "plt.plot(acc_dict['val_1'], linestyle='dashed',label='lr = 1e-3 - validation', c = 'green')\n",
        "plt.plot(acc_dict['train_1'],label='1e-3 - train', c = 'green')\n",
        "plt.plot(acc_dict['val_2'], linestyle='dashed',label='lr = 5e-3 - validation', c = 'purple')\n",
        "plt.plot(acc_dict['train_2'],label = '5e-3 - train', c = 'purple')\n",
        "plt.title('Optimal Learning Rate')\n",
        "plt.ylabel('Accuracy (%)')\n",
        "plt.xlabel('Epoch')\n",
        "print('Maximum Test Accuracy obtained with lr = 5e-4: '+str(max(acc_dict['val_0'])))\n",
        "print('Maximum Test Accuracy obtained with lr = 1e-3: '+str(max(acc_dict['val_1'])))\n",
        "print('Maximum Test Accuracy obtained with lr = 5e-3: '+str(max(acc_dict['val_2'])))\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Maximum Test Accuracy obtained with lr = 5e-4: 65.0\n",
            "Maximum Test Accuracy obtained with lr = 1e-3: 66.0\n",
            "Maximum Test Accuracy obtained with lr = 5e-3: 67.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 579,
              "height": 459
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "qBYNBuov0rmU",
        "outputId": "a63531e4-f476-47aa-9aee-d04f7e895e83"
      },
      "source": [
        "plt.plot(acc_dict['param_norm0'],label='lr = 5e-4',c='blue')\r\n",
        "plt.plot(acc_dict['param_norm1'],label = 'lr = 1e-3',c='green')\r\n",
        "plt.plot(acc_dict['param_norm2'],label ='lr = 5e-3', c='red')\r\n",
        "plt.legend()\r\n",
        "plt.xlabel('epoch')\r\n",
        "plt.ylabel('parameter norms')\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 569,
              "height": 437
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ECKFAMOrN5Fy"
      },
      "source": [
        "In the model above, we observe something different from what we expected. Why do you think this is happening?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uBH50aWCOCnB"
      },
      "source": [
        "#@markdown Write down the answer\r\n",
        "learning_rate = 'va' #@param {type:\"string\"}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OySRfKnQGbhW"
      },
      "source": [
        "## Batch Size\n",
        "Batch size, in some cases, can also help in regularizing the models. Lower batch size leads to a noisy convergence and hence helps in converging to a broader local minima. Whereas, higher batch size lead to a smoother convergence thereby converging easily to a  deeper local minima.  This can be good or bad.\n",
        "\n",
        "In the below blcok we will train the Animal Net model with different batch sizes and see how that is going to affect the regularization performance."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2aIo8e8NQfWQ"
      },
      "source": [
        "#@title Dataloaders for Regularization\r\n",
        "data_path = pathlib.Path('.')/'afhq' # using pathlib to be compatible with all OS's\r\n",
        "img_dataset = ImageFolder(data_path/'train', transform=train_transform)\r\n",
        "\r\n",
        "#Splitting dataset\r\n",
        "reg_train_data, reg_val_data,_ = torch.utils.data.random_split(img_dataset, [250,100,14280])\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rQbO03j8GjUX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0d92879e-ed1c-4859-8499-189b009300db"
      },
      "source": [
        "args = {'lr': 5e-3,\n",
        "        'epochs': 60,\n",
        "        'momentum': 0.99,\n",
        "        'no_cuda': False\n",
        "        }\n",
        "\n",
        "batch_sizes = [32,64,128]\n",
        "acc_dict = {}\n",
        "\n",
        "for i in range(len(batch_sizes)):\n",
        "    model = Animal_Net()\n",
        "    #Creating train_loader and Val_loader\n",
        "    reg_train_loader = torch.utils.data.DataLoader(reg_train_data,batch_size=batch_sizes[i],worker_init_fn=seed_worker)\n",
        "    reg_val_loader = torch.utils.data.DataLoader(reg_val_data,batch_size=1000,worker_init_fn=seed_worker)\n",
        "    val_acc, train_acc,param_norm,_,_ = main(args,model,reg_train_loader,reg_val_loader,img_test_dataset)\n",
        "    acc_dict['train_'+str(i)] = train_acc\n",
        "    acc_dict['val_'+str(i)] = val_acc\n",
        "    acc_dict['param_norm'+str(i)] = param_norm"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ifx1rCTiG-tW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        },
        "outputId": "90058798-7067-4c99-911b-8fd2d17b7625"
      },
      "source": [
        "plt.plot(acc_dict['train_0'], label='mb_size =' + str(batch_sizes[0]), c = 'blue')\n",
        "plt.plot(acc_dict['val_0'], linestyle='dashed', c = 'blue')\n",
        "\n",
        "plt.plot(acc_dict['train_1'], label='mb_size =' + str(batch_sizes[1]), c = 'orange')\n",
        "plt.plot(acc_dict['val_1'], linestyle='dashed', c = 'orange')\n",
        "plt.plot(acc_dict['train_2'], label='mb_size =' + str(batch_sizes[2]), c = 'green')\n",
        "plt.plot(acc_dict['val_2'], linestyle='dashed', c = 'green')\n",
        "print('maximum accuracy for mini batchsize = 32: '+str(max(acc_dict['val_0'])))\n",
        "print('maximum accuracy for mini batchsize = 64: '+str(max(acc_dict['val_1'])))\n",
        "print('maximum accuracy for mini batchsize = 128: '+str(max(acc_dict['val_2'])))\n",
        "\n",
        "plt.title('Optimal Batch Size')\n",
        "plt.ylabel('Accuracy (%)')\n",
        "plt.xlabel('Epoch (Sec)')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "62.0\n",
            "59.0\n",
            "61.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 579,
              "height": 459
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S6Kp_jf37nnS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "outputId": "5eba8889-a71e-4c2b-daa8-82a4235cc805"
      },
      "source": [
        "plt.plot(acc_dict['param_norm0'],c='blue',label='mb_size =' + str(batch_sizes[0]))\r\n",
        "plt.plot(acc_dict['param_norm1'],c='orange',label='mb_size =' + str(batch_sizes[1]))\r\n",
        "plt.plot(acc_dict['param_norm2'],c='green',label='mb_size =' + str(batch_sizes[2]))\r\n",
        "plt.xlabel('epoch')\r\n",
        "plt.ylabel('Parameter Norm')\r\n",
        "plt.legend()\r\n",
        "plt.show()\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 569,
              "height": 437
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l_Sbvul0O9AP"
      },
      "source": [
        "Here what observation can you make for different batch size. Why do you think this is happening?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tPsOclZ2PIvN"
      },
      "source": [
        "#@markdown Write down the answer\r\n",
        "batch_size = 'value' #@param {type:\"string\"}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i1dLTAENW0_q"
      },
      "source": [
        "#Section 2: Pruning\n",
        "(Time Estimate: 30 min from start)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZWNq6aknCZQD"
      },
      "source": [
        "Before we dig deeper into pruning let's take a small detour and calculate the inference time (time taken for one forward pass during testing) and number of parameters of the biggest model we trained this week.\n",
        "\n",
        "\n",
        "```\n",
        "class Animal_Net_Dropout(nn.Module):\n",
        "    def __init__(self):\n",
        "        torch.manual_seed(32)\n",
        "        super(Animal_Net_Dropout, self).__init__()\n",
        "        self.fc1 = nn.Linear(3*32*32, 248)\n",
        "        self.fc2 = nn.Linear(248, 210)\n",
        "        self.fc3 = nn.Linear(210, 3)\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bFzHLFwIrUxP"
      },
      "source": [
        "##Exercise 1: Calculating Inference"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oWuJ2IKxlUTU"
      },
      "source": [
        "Now calculate by hand the exact total number of parameters and fill in the code below to calculate the average inference time of the above model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mu27sYWFlfM8"
      },
      "source": [
        "def calculate_inference(N):\n",
        "\n",
        "    ####################################################################\n",
        "    # Fill in all missing code below (...),\n",
        "    # then remove or comment the line below to test your function\n",
        "    raise NotImplementedError(\"Define the calculate inference function\")\n",
        "    ####################################################################\n",
        "\n",
        "    total_time = 0.0\n",
        "    model = Dropout_Animal_Net()\n",
        "    model.eval()\n",
        "    \n",
        "    for i in range(N):\n",
        "        X = ....\n",
        "        with ...:\n",
        "            start_time = time.time()\n",
        "            y = ...\n",
        "            end_time = time.time()\n",
        "            total_time ...\n",
        "\n",
        "    print(f'Inference time of the above network is: {total_time/N}')\n",
        "\n",
        "##uncomment to run\n",
        "#calculate_inference(1000)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "76wEp1RzoTIr"
      },
      "source": [
        "[Click for Solution](https://github.com/CIS-522/course-content/blob/main/tutorials/W05_Regularization/solutions/W5_Tutorial2_Ex01.py)\n",
        "\n",
        "Example Output:\n",
        "\n",
        "![Screenshot from 2021-02-12 14-04-49.png]()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9lyHZXH4EL1l",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "034ff672-0e22-453c-93a1-1b613c45d629"
      },
      "source": [
        "def calculate_inference(N):\n",
        "\n",
        "    \"\"\"\n",
        "        Calculates the average inference time of a model\n",
        "    \"\"\"\n",
        "\n",
        "    total_time = 0.0\n",
        "    model = Dropout_Animal_Net()\n",
        "    model.eval()\n",
        "    \n",
        "    for i in range(N):\n",
        "        X = torch.rand((1,3,32,32))\n",
        "        with torch.no_grad():\n",
        "            start_time = time.time()\n",
        "            y = model(X)\n",
        "            end_time = time.time()\n",
        "            total_time += end_time - start_time\n",
        "\n",
        "    print(f'Inference time of the above network is: {total_time/N}')\n",
        "\n",
        "calculate_inference(1000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Inference time of the above network is: 0.00026513123512268065\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bZgxj7RiGKsw"
      },
      "source": [
        "#@markdown Write down the answer\n",
        "number_of_parameters = 'value' #@param {type:\"string\"}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E_IDYTpEHO9c"
      },
      "source": [
        "This relatively small model with approximately 815,000 parameters when trained on 100 training samples with takes about 2 min to finish training with an inference time of approx 3msec,  which can be used for real time applications. But this obviously isn't the best network we can do. We can use a bigger network and train on bigger dataset, infact the entire Animal Faces Dataset consists of about around 15000 training data.Also, we have to keep in mind that the images we are using are of very low resolution(32X32), the sizes of the images in the original dataset is 512X512. While this will definetly improve the performance of the model it will also increase the resources needed to handle the model.\n",
        "\n",
        "Google is known for training very big language models and recently it trained a [trillion paramter](https://thenextweb.com/neural/2021/01/13/googles-new-trillion-parameter-ai-language-model-is-almost-6-times-bigger-than-gpt-3/) model. This is almost 1.2e6 times bigger than the model we just trained. So it is sufficient to say that these big models need intense compute power to train while also becomeing harder to deploy and get real time inference on smaller micro - proccesors. \n",
        "\n",
        "This is where regularization and pruning come in very handy. Until now you should have noticed that the Frobenious norm of the regularized models that we trained tend to be smaller than those of unregualrized models. This indicates that the regualarization is shrinking the weights (making the model sparser) while improving the test performance. \n",
        "\n",
        "While methods like L1 regulartization promote implicit sparsity, in pruning we explicitly set a few weights of the tained model to zero and then retrain the model to adjust the other weights. This reduces the memory consumption, improves inference and helps the planet :)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y5IUWMKh8k_q",
        "cellView": "form"
      },
      "source": [
        "#@title Video :  Pruning\n",
        "try: t2;\n",
        "except NameError: t2=time.time()\n",
        "\n",
        "from IPython.display import YouTubeVideo\n",
        "video = YouTubeVideo(id=\"7S5LA2OFdzs\", width=854, height=480, fs=1)\n",
        "print(\"Video available at https://youtube.com/watch?v=\" + video.id)\n",
        "\n",
        "video"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_eJuNAOiMJuN"
      },
      "source": [
        "One of the most common methods of pruning a NN is to zero out a certain percentage of parameters based on their L1 norm. We don't actually remove the parameters because that makes forward computations difficult.\n",
        "\n",
        "Luckily we have Pytorch's torch.nn.utils.prune methods to play around and test pruning."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Avjo2i_mKDf"
      },
      "source": [
        "##Exercise 2: L1Prune\n",
        "Before we train a pruned model let us write a function to prunes a model. Use [prune.L1Unstructured](https://pytorch.org/docs/stable/generated/torch.nn.utils.prune.l1_unstructured.html#torch.nn.utils.prune.l1_unstructured) method."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BTz0Ae1cmjXy"
      },
      "source": [
        "def l1_unstructured(model,prune_percent_weight,prune_percent_bias = 0):\n",
        "    ####################################################################\n",
        "    # Fill in all missing code below (...),\n",
        "    # then remove or comment the line below to test your function\n",
        "    raise NotImplementedError(\"Define the calculate inference function\")\n",
        "    ####################################################################\n",
        "\n",
        "    for name, module in trained_model.named_modules():\n",
        "        if isinstance(module, torch.nn.Conv2d) or isinstance(module, torch.nn.Linear):\n",
        "            ...\n",
        "            ...\n",
        "\n",
        "            print(\n",
        "                \"Sparsity in {}: {:.2f}%\".format(name,\n",
        "                    100. * float(torch.sum(module.weight == 0))\n",
        "                    / float(module.weight.nelement())\n",
        "                )\n",
        "            )\n",
        "##uncomment to run the test\n",
        "# test_model = Animal_Net()\n",
        "# prune_percent = 0.15\n",
        "# prune_l1_unstructured(test_model,0.15)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BDpqNdJFqS5O"
      },
      "source": [
        "[Click for Solution](https://github.com/CIS-522/course-content/blob/main/tutorials/W05_Regularization/solutions/W5_Tutorial2_Ex02.py)\n",
        "\n",
        "Example Output:\n",
        "\n",
        "![Screenshot from 2021-02-12 18-52-30.png]()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NR4BwxLOmJbv"
      },
      "source": [
        "def prune_l1_unstructured(model,prune_percent_weight,prune_percent_bias = 0.0,verbose=True):\n",
        "\n",
        "    for name, module in model.named_modules():\n",
        "        if isinstance(module, torch.nn.Conv2d) or isinstance(module, torch.nn.Linear):\n",
        "            prune.l1_unstructured(module, name='weight', amount=prune_percent_weight)\n",
        "            prune.l1_unstructured(module, name='bias', amount=prune_percent_bias)\n",
        "            if(verbose):\n",
        "                print(\n",
        "                    \"Sparsity in {}: {:.2f}%\".format(name,\n",
        "                        100. * float(torch.sum(module.weight == 0))\n",
        "                        / float(module.weight.nelement())\n",
        "                    )\n",
        "                )\n",
        "\n",
        "##uncomment to run the test\n",
        "test_model = Animal_Net()\n",
        "prune_percent = 0.15\n",
        "prune_l1_unstructured(test_model,0.15)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2iNU0Uo5mIpC"
      },
      "source": [
        "In the section below, you will see the working of a very simple pruning technique which prunes a percentage of the parameters based on their weights."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OcROLun4YbeG"
      },
      "source": [
        "args = {'test_batch_size': 1000,\n",
        "        'epochs': 200,\n",
        "        'lr': 5e-3,\n",
        "        'momentum': 0.9,\n",
        "        'no_cuda': False\n",
        "        }\n",
        "\n",
        "acc_dict = {}\n",
        "model = Big_Animal_Net()\n",
        "prune_percent = 0.5\n",
        "\n",
        "print(\"Training a randomly initialized model\")\n",
        "val_acc, train_acc, _, trained_model ,_ = main(args,model,train_loader,val_loader,img_test_dataset)\n",
        "\n",
        "##pruning a model\n",
        "print('Pruning and verifying and model:')\n",
        "prune_l1_unstructured(trained_model,prune_percent)\n",
        "\n",
        "#training the pruned model\n",
        "print(\"Training a pruned model\")\n",
        "val_acc_prune, train_acc_prune, _, pruned_model ,_ = main(args,trained_model.to('cpu'),train_loader,val_loader,img_test_dataset)\n",
        "\n",
        "val_acc_prune = [val_acc_prune[0]]*args['epochs'] + val_acc_prune\n",
        "train_acc_prune = [train_acc_prune[0]]*args['epochs'] + train_acc_prune\n",
        "plt.plot(val_acc,label='Val',c='blue',ls = 'dashed')\n",
        "plt.plot(train_acc,label='Train',c='blue',ls = 'solid')\n",
        "plt.plot(val_acc_prune,label='Val Prune',c='red',ls = 'dashed')\n",
        "plt.plot(train_acc_prune,label='Train Prune',c='red',ls = 'solid')\n",
        "plt.title('Pruning')\n",
        "plt.ylabel('Accuracy (%)')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "720HNoTNrcLQ"
      },
      "source": [
        "Now change the prune_percent and report the percentage at which the model underfits."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YQm_yEshrnMp"
      },
      "source": [
        "#@markdown Write down your discussion\n",
        "pruning_percent = 'value' #@param {type:\"string\"}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xyTIvCgsvZ3x"
      },
      "source": [
        "Will the model work well if we only train a model the above unpruned percent of parameters in the model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fsr3vqmMvZ3z"
      },
      "source": [
        "#@markdown Write down your discussion\n",
        "under_parameterized_model = 'value' #@param {type:\"string\"}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z8eoVO3_SUCa"
      },
      "source": [
        "In the above pruning technique after pruning the network, how do you think the performance of the will change if we re-initialize the weights while maintaing the prune mask?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aOoExJGgTB_n"
      },
      "source": [
        "#@markdown Write down your discussion\n",
        "pruning_re_init = 'value' #@param {type:\"string\"}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qp3TtsUsZT_N"
      },
      "source": [
        "## Lottery Tickets\n",
        "(Time Estimate: 50min from start)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jyFEk3srVf39"
      },
      "source": [
        "The lottery ticket hypothesis claims that \" A dense randomly initialized NN contains a subnetwork that is initialzed such that when trained in isolation it can match the test accuracy of the original network after training for at most same number of iterations\" i.e. a pruned model when reinitialized with the same weights will can match the test accuracy of the denser model. If the initialization changes the accuracy match is no longer guaranteed.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7qwr_pTEVVne"
      },
      "source": [
        "Here we train the following networks:\n",
        "\n",
        "\n",
        "1.   An unregularized model for 200 epochs\n",
        "2.   A Pruned model with the weights reinitialized to the same values.\n",
        "3.   A pruned model with weights reinitialized with Xavier Initialization\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Su8AFvyZMxsY"
      },
      "source": [
        "args = {'test_batch_size': 1000,\n",
        "        'epochs': 200,\n",
        "        'lr': 1e-3,\n",
        "        'momentum': 0.9,\n",
        "        'no_cuda': False,\n",
        "        }\n",
        "\n",
        "acc_dict = {}\n",
        "init_model = Big_Animal_Net()\n",
        "xavier_model = Big_Animal_Net()\n",
        "prune_percent = 0.4\n",
        "\n",
        "#Xavier Initilaization for one of the two models\n",
        "for name, module in xavier_model.named_modules():\n",
        "    if isinstance(module, torch.nn.Conv2d) or isinstance(module, torch.nn.Linear):\n",
        "        torch.nn.init.xavier_uniform_(module.weight)\n",
        "\n",
        "print('Training the full model')\n",
        "val_acc, train_acc, _, trained_model ,_ = main(args,copy.deepcopy(xavier_model),train_loader,val_loader,img_test_dataset)\n",
        "\n",
        "\n",
        "#prune the trained model\n",
        "prune_l1_unstructured(trained_model,prune_percent,verbose=False)\n",
        "\n",
        "#initialize masks for the initialzed model and xavier model\n",
        "for name, module in init_model.named_modules():\n",
        "    if isinstance(module, torch.nn.Conv2d) or isinstance(module, torch.nn.Linear):\n",
        "        prune.identity(module, name='weight')\n",
        "        prune.identity(module, name='bias')\n",
        "\n",
        "for name, module in xavier_model.named_modules():\n",
        "    if isinstance(module, torch.nn.Conv2d) or isinstance(module, torch.nn.Linear):\n",
        "        prune.identity(module, name='weight')\n",
        "        prune.identity(module, name='bias')\n",
        "\n",
        "init_modules = [[name,module] for name, module in init_model.named_modules()]\n",
        "xavier_modueles = [[name,module] for name, module in xavier_model.named_modules()]\n",
        "trained_modules = [[name,module] for name, module in trained_model.named_modules()]\n",
        "\n",
        "for i in range(len(init_modules)):\n",
        "    if isinstance(init_modules[i][1], torch.nn.Conv2d) or isinstance(init_modules[i][1], torch.nn.Linear):\n",
        "        init_modules[i][1].weight_mask = copy.deepcopy(trained_modules[i][1].weight_mask) \n",
        "        init_modules[i][1].bias_mask = copy.deepcopy(trained_modules[i][1].bias_mask)\n",
        "\n",
        "for i in range(len(xavier_modueles)):\n",
        "    if isinstance(xavier_modueles[i][1], torch.nn.Conv2d) or isinstance(xavier_modueles[i][1], torch.nn.Linear):\n",
        "        xavier_modueles[i][1].weight_mask = copy.deepcopy(trained_modules[i][1].weight_mask) \n",
        "        xavier_modueles[i][1].bias_mask = copy.deepcopy(trained_modules[i][1].bias_mask)\n",
        "\n",
        "\n",
        "print('Training the pruned and Xavier model')\n",
        "val_acc_lottery_x, train_acc_lottery_x, _, pruned_model_x ,_ = main(args,xavier_model,train_loader,val_loader,img_test_dataset)\n",
        "print('Training the pruned Init model')\n",
        "val_acc_lottery, train_acc_lottery, _, pruned_model ,_ = main(args,init_model,train_loader,val_loader,img_test_dataset)\n",
        "\n",
        "plt.plot(val_acc,c='blue',ls = 'dashed')\n",
        "plt.plot(train_acc,label='Train',c='blue',ls = 'solid')\n",
        "plt.plot(val_acc_lottery,c='red',ls = 'dashed')\n",
        "plt.plot(train_acc_lottery,label='Train - Random',c='red',ls = 'solid')\n",
        "plt.plot(val_acc_lottery_x,c='green',ls = 'dashed')\n",
        "plt.plot(train_acc_lottery_x,label='Train - Xavier',c='green',ls = 'solid')\n",
        "plt.title('Lottery Tickets')\n",
        "plt.ylabel('Accuracy (%)')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EYTqX4Xv0DNn"
      },
      "source": [
        "Why according to you does the first train epoch of lottery ticket have such a high train accuracy?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d8LCQbv80DNt"
      },
      "source": [
        "#@markdown Write down your discussion\n",
        "lottery_tickets = 'value' #@param {type:\"string\"}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vUtwddoZ9J4P"
      },
      "source": [
        "#Distillation\n",
        "(Time Estimate: 80 min from start)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0JPlxMQOpVmE"
      },
      "source": [
        "Bigger neural nets are better for model performance but require significant memory, while smaller networks tend be be less accurate but are easier to deploy and use. \n",
        "\n",
        "Distillation is a technique which allows us to train smaller networks such that they mimic the outputs of the bigger network. The bigger network is called the teacher network wheras the smaller one is the parent network. \n",
        "\n",
        "Distillation begins by training a teacher network. It then trains the student network with both the original labels and \"soft\" labels--the output of the teacher model. This lets us train the student network on unlabelled datasets, using the labeled given by the teacher network. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T0cLGyqf8oUd",
        "cellView": "form"
      },
      "source": [
        "#@title Video : Distillation\n",
        "try: t3;\n",
        "except NameError: t3=time.time()\n",
        "\n",
        "from IPython.display import YouTubeVideo\n",
        "video = YouTubeVideo(id=\"y_KzDpklMmE\", width=854, height=480, fs=1)\n",
        "print(\"Video available at https://youtube.com/watch?v=\" + video.id)\n",
        "video"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gHA10EuJrmjV"
      },
      "source": [
        "class Small_Animal_Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        torch.manual_seed(32)\n",
        "        super(Small_Animal_Net, self).__init__()\n",
        "        self.fc1 = nn.Linear(3*32*32, 32)\n",
        "        self.fc2 = nn.Linear(32, 3)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = x.view(x.shape[0],-1)\n",
        "        x = F.leaky_relu(self.fc1(x))\n",
        "        x = self.fc2(x)\n",
        "        output = F.log_softmax(x, dim=1)\n",
        "        return output"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n-zsV74w3P8m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "5cebb181-e7a1-4f0e-dff0-d1e589b88807"
      },
      "source": [
        "args = {'test_batch_size': 1000,\n",
        "        'epochs': 200,\n",
        "        'momentum': 0.9,\n",
        "        'no_cuda': False,\n",
        "        'lr' : 5e-3,\n",
        "        'cross_entropy':True\n",
        "        }\n",
        "\n",
        "Bmodel = Big_Animal_Net()\n",
        "Smodel = Small_Animal_Net()\n",
        "\n",
        "val_acc_big, train_acc_big, _, trained_big_model ,_ = main(args,Bmodel,train_loader,val_loader,img_test_dataset)\n",
        "val_acc_small, train_acc_small, _, _ ,_ = main(args,Smodel,train_loader,val_loader,img_test_dataset)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "        <progress\n",
              "            value='200'\n",
              "            max='100',\n",
              "            style='width: 100%'\n",
              "        >\n",
              "            200\n",
              "        </progress>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "        <progress\n",
              "            value='200'\n",
              "            max='100',\n",
              "            style='width: 100%'\n",
              "        >\n",
              "            200\n",
              "        </progress>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6b8BnQzOrb7t"
      },
      "source": [
        "Loss Function:\n",
        "\n",
        "        L = (1 - alpha)*CE(outputs,ground_truth) + alpha * (T**2) *CE(outputs,soft_targets)\n",
        "        L = (1 - alpha)*CE(outputs,ground_truth) + alpha * (T**2) *KL(outputs,soft_targets)\n",
        "\n",
        "\n",
        "Cross Entropy loss and KL Divergence are both related by: H(p,q) = H(p) + KL(p,q). Here p is the probability distribution of soft_outputs which are constant and hence we can omit from the loss function.\n",
        "\n",
        "Here alpha and temperature are hyper parameters where temperatures is used to smoothen the outputs of the parent network."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nKhrkVwY6Wlq"
      },
      "source": [
        "def distillation_loss(args,soft_outputs, pred_logits, target):\n",
        "\n",
        "    alpha = args['alpha']\n",
        "    T = args['temperature']\n",
        "    dist_loss = (1. - alpha) * F.cross_entropy(pred_logits, target) + \\\n",
        "                    (alpha * (T ** 2)) * F.kl_div(F.log_softmax(pred_logits/T, dim=1),\n",
        "                             F.softmax(soft_outputs/T, dim=1))\n",
        "\n",
        "    return dist_loss"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dH5qZx-HQcDT"
      },
      "source": [
        "def train_softmax_distillation(args, student_model,parent_model, device, train_loader, optimizer, epoch):\n",
        "    \n",
        "    student_model.train()\n",
        "    parent_model.eval()\n",
        "    for batch_idx, (data, target) in enumerate(train_loader):\n",
        "        data, target = data.to(device), target.to(device)\n",
        "        optimizer.zero_grad()\n",
        "        soft_outputs = parent_model(data)\n",
        "        pred_logits = student_model(data)\n",
        "        loss = distillation_loss(args,soft_outputs.detach(), pred_logits, target)\n",
        "        loss.backward()\n",
        "        optimizer.step()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mXaMJOfVP0TL"
      },
      "source": [
        "def distilation_main(args, teacher_model,student_model,train_loader,val_loader):\n",
        "\n",
        "    use_cuda = not args['no_cuda'] and torch.cuda.is_available()\n",
        "    device = torch.device('cuda' if use_cuda else 'cpu')\n",
        "\n",
        "    student_model = student_model.to(device)\n",
        "    teacher_model = teacher_model.to(device)\n",
        "    optimizer = optim.SGD(student_model.parameters(), lr=args['lr'], momentum=args['momentum'])\n",
        "    progress_bar = display(progress(0, args['epochs']), display_id=True)\n",
        "    val_acc_list = []\n",
        "    train_acc_list = []\n",
        "    for epoch in range(1, args['epochs'] + 1):\n",
        "        train_softmax_distillation(args, student_model,teacher_model, device, train_loader, optimizer, epoch)\n",
        "        train_acc = test(student_model,device,train_loader,'Train')\n",
        "        val_acc = test(student_model,device,val_loader,'Val')\n",
        "        val_acc_list.append(val_acc)\n",
        "        train_acc_list.append(train_acc)\n",
        "        progress_bar.update(progress(epoch+1, args['epochs']))\n",
        "\n",
        "    return val_acc_list, train_acc_list, student_model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rp-5pGaeubLc"
      },
      "source": [
        "args = {'test_batch_size': 1000,\n",
        "        'epochs': 200,\n",
        "        'momentum': 0.9,\n",
        "        'no_cuda': False,\n",
        "        'lr' : 5e-3,\n",
        "        'alpha': 1,\n",
        "        'temperature': 40\n",
        "        }\n",
        "\n",
        "student_model = Small_Animal_Net()\n",
        "\n",
        "val_acc_st, train_acc_st, _, = distilation_main(args,trained_big_model,student_model,train_loader,val_loader)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YtRFEj_Uwq1l"
      },
      "source": [
        "plt.plot(val_acc_small,label='Val - Small',c='red',ls = 'dashed')\n",
        "plt.axhline(y = max(val_acc_small),c = 'red')\n",
        "plt.plot(val_acc_st,label='Val - S',c='green',ls = 'dashed')\n",
        "plt.axhline(y = max(val_acc_st),c = 'green')\n",
        "plt.title('Distillation')\n",
        "plt.ylabel('Accuracy (%)')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZXhCP6P5Snd_"
      },
      "source": [
        "This method not only provides a way to train small and better networks but also gives us a chance to train small networks on more data using the soft labels from the heavily optimized parent networks."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zUu5orrg0UHc"
      },
      "source": [
        "What other techniques can you use to reduce the dimensionality or size of the big network or the input data?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ayCEX2zp0UHl"
      },
      "source": [
        "#@markdown Write down your discussion\n",
        "distillation = 'value' #@param {type:\"string\"}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8SsdI6tVz96F"
      },
      "source": [
        "Do you think generalization helps when you have infinate data with available?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8rLGFyIPz96J"
      },
      "source": [
        "#@markdown Write down your discussion\n",
        "data = 'value' #@param {type:\"string\"}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MJZMeMTm1NG6"
      },
      "source": [
        "Which regualarization technique from this week do you think had the biggest effect on the network and why do you think so? Can you apply all of them on the same network?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2d99_VOK1NHG"
      },
      "source": [
        "#@markdown Write down your discussion\n",
        "complete = 'value' #@param {type:\"string\"}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b5HDVVEljKGQ"
      },
      "source": [
        "# Adversarial  attacks\n",
        "Time Estimate: (110 minutes from start)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "03YLRgGemuN6",
        "cellView": "form"
      },
      "source": [
        "#@title Video : Adversarial\n",
        "try: t4;\n",
        "except NameError: t4=time.time()\n",
        "\n",
        "from IPython.display import YouTubeVideo\n",
        "video = YouTubeVideo(id=\"2e-PxxlGfpM\", width=854, height=480, fs=1)\n",
        "print(\"Video available at https://youtube.com/watch?v=\" + video.id)\n",
        "video"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VtRxB698CTfG"
      },
      "source": [
        "---\n",
        "# Wrap up"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P5-HZSWcCbr3"
      },
      "source": [
        "## Submit responses"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FCJJf7OFk8SU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "outputId": "07079031-ffde-4388-83c4-9fa99d07efa0"
      },
      "source": [
        "#@markdown #Run Cell to Show Airtable Form\n",
        "#@markdown ##**Confirm your answers and then click \"Submit\"**\n",
        "\n",
        "import time\n",
        "import numpy as np\n",
        "from IPython.display import IFrame\n",
        "\n",
        "def prefill_form(src, fields: dict):\n",
        "  '''\n",
        "  src: the original src url to embed the form\n",
        "  fields: a dictionary of field:value pairs,\n",
        "  e.g. {\"pennkey\": my_pennkey, \"location\": my_location}\n",
        "  '''\n",
        "  prefills = \"&\".join([\"prefill_%s=%s\"%(key, fields[key]) for key in fields])\n",
        "  src = src + prefills\n",
        "  src = \"+\".join(src.split(\" \"))\n",
        "  return src\n",
        "\n",
        "\n",
        "#autofill time if it is not present\n",
        "try: t0;\n",
        "except NameError: t0 = time.time()\n",
        "try: t1;\n",
        "except NameError: t1 = time.time()\n",
        "try: t2;\n",
        "except NameError: t2 = time.time()\n",
        "try: t3;\n",
        "except NameError: t3 = time.time()\n",
        "try: t4;\n",
        "except NameError: t4 = time.time()\n",
        "\n",
        "#autofill fields if they are not present\n",
        "#a missing pennkey and pod will result in an Airtable warning\n",
        "#which is easily fixed user-side.\n",
        "try: my_pennkey;\n",
        "except NameError: my_pennkey = \"\"\n",
        "try: my_pod;\n",
        "except NameError: my_pod = \"\"\n",
        "try: question_of_the_week;\n",
        "except NameError: question_of_the_week = \"\"\n",
        "try: learning_rate;\n",
        "except NameError: learning_rate = \"\"\n",
        "try: batch_size;\n",
        "except NameError: batch_size = \"\"\n",
        "try: number_of_parameters;\n",
        "except NameError: number_of_parameters = \"\"\n",
        "try: pruning_percent;\n",
        "except NameError: pruning_percent = \"\"\n",
        "try: under_parameterized_model;\n",
        "except NameError: under_parameterized_model = \"\"\n",
        "try: pruning_re_init;\n",
        "except NameError: pruning_re_init = \"\"\n",
        "try: lottery_tickets;\n",
        "except NameError: lottery_tickets = \"\"\n",
        "try: distillation;\n",
        "except NameError: distillation = \"\"\n",
        "try: complete;\n",
        "except NameError: complete = \"\"\n",
        "try: data;\n",
        "except NameError: data = \"\"\n",
        "\n",
        "times = [(t-t0) for t in [t1,t2,t3,t4]]\n",
        "\n",
        "fields = {\"my_pennkey\": my_pennkey,\n",
        "          \"my_pod\": my_pod,\n",
        "          \"question_of_the_week\":question_of_the_week,\n",
        "          \"learning_rate\":learning_rate,\n",
        "          \"batch_size\":batch_size,\n",
        "          \"number_of_parameters\":number_of_parameters,\n",
        "          \"pruning_percent\":pruning_percent,\n",
        "          \"under_parameterized_model\":under_parameterized_model,\n",
        "          \"pruning_re_init\":pruning_re_init,\n",
        "          \"lottery_tickets\": lottery_tickets,\n",
        "          \"distillation\": complete,\n",
        "          \"cumulative_times\": times\n",
        "        }\n",
        "           \n",
        "print(fields)\n",
        "src=\"https://airtable.com/embed/shr2XivycAmugUavT?\"\n",
        "\n",
        "#now instead of the original source url, we do: src = prefill_form(src, fields)\n",
        "display(IFrame(src = prefill_form(src, fields), width = 800, height = 400))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'my_pennkey': 'moji', 'my_pod': 'discreet-reindeer', 'question_of_the_week': 'brodha V', 'learning_rate': 'va', 'batch_size': 'value', 'number_of_parameters': 'value', 'pruning_percent': 'value', 'under_parameterized_model': 'value', 'pruning_re_init': 'value', 'lottery_tickets': 'value', 'distillation': 'value', 'cumulative_times': [2.5987625122070312e-05, 4.9591064453125e-05, 7.867813110351562e-05, 0.0001010894775390625]}\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "        <iframe\n",
              "            width=\"800\"\n",
              "            height=\"400\"\n",
              "            src=\"https://airtable.com/embed/shr2XivycAmugUavT?prefill_my_pennkey=moji&prefill_my_pod=discreet-reindeer&prefill_question_of_the_week=brodha+V&prefill_learning_rate=va&prefill_batch_size=value&prefill_number_of_parameters=value&prefill_pruning_percent=value&prefill_under_parameterized_model=value&prefill_pruning_re_init=value&prefill_lottery_tickets=value&prefill_distillation=value&prefill_cumulative_times=[2.5987625122070312e-05,+4.9591064453125e-05,+7.867813110351562e-05,+0.0001010894775390625]\"\n",
              "            frameborder=\"0\"\n",
              "            allowfullscreen\n",
              "        ></iframe>\n",
              "        "
            ],
            "text/plain": [
              "<IPython.lib.display.IFrame at 0x7ff22e53d908>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HKn5d3CCC05w"
      },
      "source": [
        "## Feedback\n",
        "How could this session have been better? How happy are you in your group? How do you feel right now?\n",
        "\n",
        "Feel free to use the embeded form below or use this link:\n",
        "<a target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://airtable.com/shrNSJ5ECXhNhsYss\">https://airtable.com/shrNSJ5ECXhNhsYss</a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HIvhG6VZ8zez"
      },
      "source": [
        "display(IFrame(src=\"https://airtable.com/embed/shrNSJ5ECXhNhsYss?backgroundColor=red\", width = 800, height = 400))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7WcXYkb0vDvl"
      },
      "source": [
        "## Homeworks\n",
        "\n",
        "1.   [Understanding Generalization](https://docs.google.com/document/d/1XOaTXYBleQlDNFM1-t512RHfJXRwA4-LIejuBA6pbLY/edit)\n",
        "2.   [Adversarial Attacks](https://)"
      ]
    }
  ]
}